Ci bi mu melGis bi ci topp
AI in Public Budget Forecasting
Liggéeyukaay yi
GUIDE usine
IA ci Meteo
GraphCast and GenCast are machine-learning weather-forecasting systems trained on historical atmospheric data.
Déglul tegtal biiIA-nettali · ~1 min
Ci xët wii1 simili jàng
Résumé
GraphCast produces deterministic forecasts; GenCast generates probabilistic ensembles.
Plongeur bu xóot
GraphCast uses a graph neural network to predict global weather states at six-hour steps, trained on ERA5 reanalysis. The Science paper reports forecast-skill comparisons across variables and lead times against operational systems. GenCast is a probabilistic model that generates ensembles and is evaluated on medium-range forecasts and extreme events. The results are scoped to the paper’s data, baselines and verification metrics; they are not a guarantee for every region, variable or extreme event.
Doxal ci àdduna dëgg
GraphCast predicts atmospheric variables forward in six-hour increments in the published setup.
ERA5 reanalysis supplies historical fields used in GraphCast training.
GenCast samples multiple plausible trajectories to represent forecast uncertainty.
Published evaluations compare model forecasts with specified operational baselines and weather variables.
Ëlëgu IA ci xam luy am ci klimaa bi
Use current operational forecasts for time-sensitive decisions and treat research benchmarks as evidence about tested cases. Compare forecasts by variable, lead time, region and event type. The papers do not establish that data-driven systems universally outperform physics-based forecasting across all conditions.
Teg ko ci jëf
Lu tax mu am solo
- Kontekst bi ak sàrt yi. Xeetu liggéey bi mooy wane ndax xalaati IA yi dina ñu mëna wéy di jëflante ak dëggantaan.
- Xool kalite. Teg domen yi deñuy indi jafe-jafe ci ni njuumte yi di doxee ak ci xeetu saytu yi.
- Tabax tànneef. Dugalug liggéey bu baax dafay méngale kàttan xarala yi ak def liggéey bi ci kanam.
Moytul
- Wareef yiñ tëral mën nañu dindi prototype yu am doole yi.
- Done yu am taarix mën nañu tënk luy lore ci yenn askan.
- Sistem yu yàgg yi mën nañu indi ay jafe-jafe ci lëkkaloo ak njëg yu nëbbu.
Jéego yi ci topp
- Boole ay kàngam ci domen bi, dalee ko ci kaadar jafe-jafe yi ba ci jàngat bi.
- Nafar ay yoon ngir saytu ak ay këyit balaa ngay tàmbali.
- Teela xool ni ñuy sàmmoonte ak seeni wareef ci wàllu kaaraange.
- Defar ko ci ay fase yu leer ci taxawal ak dellu ginaaw.
Weyal di banneexu
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the AI in Weather Forecasting quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
What is AI in Weather Forecasting?
GraphCast and GenCast are machine-learning weather-forecasting systems trained on historical atmospheric data. GraphCast produces deterministic forecasts; GenCast generates probabilistic ensembles.
Ban setu done la xeetu meteo IA yu mag yi melni GraphCast di njëkka tàggatoo?
GraphCast ak yeneen xeeti model yu mel noonu, dañu leen tàggat ci ECMWF's ERA5 reanalyse, biy joxe fukki at ci done atmosphere global yu méngoo waxtu bu nekk.
Lan moo wuutale xeetu IA bu melni GraphCast ak xayma klimaa bi fi yàgg a nekk?
NWP dafay saafara equation fluide-dynamique ci anamu lim, ci noonu la IA di modele motif-match ci diggante yiñ jàngee ci done yu yàgg yi.
naka la ko GraphCast mënee defaree ab meteo bu 10 fan ci jéego bu 6 waxtu ci meteo?
GraphCast dafay wax luy waaja am 6 waxtu ci kanam, ba noppi mu joxewaat wax ji ñu wax ni dugal, baamtu lu tollu ci 40 yoon ngir yegg ci 10 fan.
Roughly how long does the GraphCast paper report for generating a 10-day global forecast on a single Google Cloud TPU v4?
The GraphCast paper reports a 10-day forecast in under one minute on a single Google Cloud TPU v4 device; this is a paper-specific hardware and model runtime, not a general guarantee for other implementations.
What result did the GraphCast paper report across its 1,380 verification targets?
The GraphCast paper reports stronger performance than the operational deterministic system on 90% of 1,380 verification targets; the figure is specific to the paper’s variables, leads and evaluation setup.
Weyal di jàng
Gid yu jëm ci loolu
Tann nañu yeneen njiit ngir topic bii